Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks

Li, Xiaoping; Xia, Lijian; Shi, Jian; Zhang, Lijie; Wang, Shaoping

doi:10.1007/s42417-023-00949-x

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Graph convolutional networks offer significant advantages in bearing fault diagnosis and classification due to their ability to process complex relational data. By leveraging the inherent structure of machinery data [18], GCNs excel in identifying complex patterns and relationships that traditional methods might miss [19]. This advanced analytical capability results in higher accuracy and efficiency in fault detection, making GCNs a powerful tool for maintaining the reliability and safety of industrial systems, thus revolutionizing the approach to bearing fault diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis

Yan,

Liao,

Zhang

et al. 2024

Machines

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In this paper, a graph convolutional network is constructed and applied for bearing fault diagnosis. Specifically, the constant-Q transform (CQT) is first adopted for spectral analysis of vibration signals, where the frequencies are distributed in the logarithmic scale. Varied frequency resolutions can be obtained to satisfy the spectral resolution requirement and reduce signal dimension. Afterwards, the CQT spectrum is modeled by a graph, where nodes are frequency bins and edges reflect the inner relationship of different bins. There are edges between the fundamental and harmonic components. Then, a two-layer graph convolutional network (GCN) is utilized to assess the significance of vibration sources within the mixed signals. Finally, the bearing faults are determined according to the output of the GCN. To the best of our knowledge, this is the first work to model the vibration signal in this graph structure. The advantage of this approach lies in the simplification of edge definitions, facilitating shared connectivity relationships between the fundamental frequency and harmonics. Its performance was compared with another state-of-the-art fault diagnosis model. Experimental results demonstrate that the proposed model obtains higher accuracy, and it is more effective in extracting discriminative features.

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Section: Introductionmentioning

confidence: 99%

Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis

Yan,

Liao,

Zhang

et al. 2024

Machines

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Helical Gear Defect Detection System Based on Symmetrized Dot Pattern and Convolutional Neural Network

Wu,

Huang,

Lai

2024

IEEE Access

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Optimal Time Frequency Fusion Symmetric Dot Pattern Bearing Fault Feature Enhancement and Diagnosis

Liang,

Song,

Liao

et al. 2024

Sensors

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Regarding the difficulty of extracting the acquired fault signal features of bearings from a strong background noise vibration signal, coupled with the fact that one−dimensional (1D) signals provide limited fault information, an optimal time frequency fusion symmetric dot pattern (SDP) bearing fault feature enhancement and diagnosis method is proposed. Firstly, the vibration signals are transformed into two−dimensional (2D) features by the time frequency fusion algorithm SDP, which can multi−scale analyze the fluctuations of signals at minor scales, as well as enhance bearing fault features. Secondly, the bat algorithm is employed to optimize the SDP parameters adaptively. It can effectively improve the distinctions between various types of faults. Finally, the fault diagnosis model can be constructed by a deep convolutional neural network (DCNN). To validate the effectiveness of the proposed method, Case Western Reserve University’s (CWRU) bearing fault dataset and bearing fault dataset laboratory experimental platform were used. The experimental results illustrate that the fault diagnosis accuracy of the proposed method is 100%, which proves the feasibility and effectiveness of the proposed method. By comparing with other 2D transformer methods, the experimental results illustrate that the proposed method achieves the highest accuracy in bearing fault diagnosis. It validated the superiority of the proposed methodology.

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Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks

Cited by 3 publications

References 38 publications

Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis

Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis

Helical Gear Defect Detection System Based on Symmetrized Dot Pattern and Convolutional Neural Network

Optimal Time Frequency Fusion Symmetric Dot Pattern Bearing Fault Feature Enhancement and Diagnosis

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